



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case						=    logging.get_logger(__name__)






def 							A							(   _lowerCamelCase							,					_lowerCamelCase=False							,					_lowerCamelCase=False    ):





							'''simple docstring'''




							_lowerCAmelCase	:				List[Any]					  =       "backbone." if is_semantic else ""

							_lowerCAmelCase	:				str					  =       []
							for i in range(config.num_hidden_layers    ):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append((F"{prefix}blocks.{i}.norm1.weight", F"beit.encoder.layer.{i}.layernorm_before.weight")    )
														rename_keys.append((F"{prefix}blocks.{i}.norm1.bias", F"beit.encoder.layer.{i}.layernorm_before.bias")    )
														rename_keys.append(
														    (F"{prefix}blocks.{i}.attn.proj.weight", F"beit.encoder.layer.{i}.attention.output.dense.weight")    )
														rename_keys.append(
														    (F"{prefix}blocks.{i}.attn.proj.bias", F"beit.encoder.layer.{i}.attention.output.dense.bias")    )
														rename_keys.append((F"{prefix}blocks.{i}.norm2.weight", F"beit.encoder.layer.{i}.layernorm_after.weight")    )
														rename_keys.append((F"{prefix}blocks.{i}.norm2.bias", F"beit.encoder.layer.{i}.layernorm_after.bias")    )
														rename_keys.append((F"{prefix}blocks.{i}.mlp.fc1.weight", F"beit.encoder.layer.{i}.intermediate.dense.weight")    )
														rename_keys.append((F"{prefix}blocks.{i}.mlp.fc1.bias", F"beit.encoder.layer.{i}.intermediate.dense.bias")    )
														rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.weight", F"beit.encoder.layer.{i}.output.dense.weight")    )
														rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.bias", F"beit.encoder.layer.{i}.output.dense.bias")    )

							# projection layer + position embeddings
							rename_keys.extend(
							    [
							        (F"{prefix}cls_token", "beit.embeddings.cls_token"),
							        (F"{prefix}patch_embed.proj.weight", "beit.embeddings.patch_embeddings.projection.weight"),
							        (F"{prefix}patch_embed.proj.bias", "beit.embeddings.patch_embeddings.projection.bias"),
							        (F"{prefix}pos_embed", "beit.embeddings.position_embeddings"),
							    ]    )

							if has_lm_head:
														# mask token + layernorm
														rename_keys.extend(
														    [
														        ("mask_token", "beit.embeddings.mask_token"),
														        ("norm.weight", "layernorm.weight"),
														        ("norm.bias", "layernorm.bias"),
														    ]    )
							else:
														# layernorm + classification head
														rename_keys.extend(
														    [
														        ("fc_norm.weight", "beit.pooler.layernorm.weight"),
														        ("fc_norm.bias", "beit.pooler.layernorm.bias"),
														        ("head.weight", "classifier.weight"),
														        ("head.bias", "classifier.bias"),
														    ]    )

							return rename_keys






def 							A							(   _lowerCamelCase							,					_lowerCamelCase							,					_lowerCamelCase=False							,					_lowerCamelCase=False    ):





							'''simple docstring'''




							for i in range(config.num_hidden_layers    ):
														_lowerCAmelCase	:				int					  =       "backbone." if is_semantic else ""
														# queries, keys and values
														_lowerCAmelCase	:				Any					  =       state_dict.pop(F"{prefix}blocks.{i}.attn.qkv.weight"    )
														_lowerCAmelCase	:				int					  =       state_dict.pop(F"{prefix}blocks.{i}.attn.q_bias"    )
														_lowerCAmelCase	:				Union[str, Any]					  =       state_dict.pop(F"{prefix}blocks.{i}.attn.v_bias"    )

														_lowerCAmelCase	:				Any					  =       in_proj_weight[
														    : config.hidden_size, :
														]
														_lowerCAmelCase	:				List[Any]					  =       q_bias
														_lowerCAmelCase	:				str					  =       in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														_lowerCAmelCase	:				Tuple					  =       in_proj_weight[
														    -config.hidden_size :, :
														]
														_lowerCAmelCase	:				Optional[Any]					  =       v_bias

														# gamma_1 and gamma_2
														# we call them lambda because otherwise they are renamed when using .from_pretrained
														_lowerCAmelCase	:				Union[str, Any]					  =       state_dict.pop(F"{prefix}blocks.{i}.gamma_1"    )
														_lowerCAmelCase	:				Any					  =       state_dict.pop(F"{prefix}blocks.{i}.gamma_2"    )

														_lowerCAmelCase	:				Optional[int]					  =       gamma_a
														_lowerCAmelCase	:				List[str]					  =       gamma_a






def 							A							(   _lowerCamelCase							,					_lowerCamelCase							,					_lowerCamelCase    ):





							'''simple docstring'''




							_lowerCAmelCase	:				int					  =       dct.pop(a_    )
							_lowerCAmelCase	:				Optional[Any]					  =       val






def 							A							(   ):





							'''simple docstring'''




							_lowerCAmelCase	:				List[Any]					  =       "http://images.cocodataset.org/val2017/000000039769.jpg"
							_lowerCAmelCase	:				str					  =       Image.open(requests.get(a_							,					stream=a_    ).raw    )
							return im






@torch.no_grad()
def 							A							(   _lowerCamelCase							,					_lowerCamelCase							,					_lowerCamelCase=False    ):





							'''simple docstring'''




							_lowerCAmelCase	:				str					  =       False if "rvlcdip" in checkpoint_url else True
							_lowerCAmelCase	:				List[Any]					  =       BeitConfig(use_absolute_position_embeddings=a_							,					use_mask_token=a_    )

							# size of the architecture
							if "large" in checkpoint_url or "dit-l" in checkpoint_url:
														_lowerCAmelCase	:				Dict					  =       1_024
														_lowerCAmelCase	:				Union[str, Any]					  =       4_096
														_lowerCAmelCase	:				Union[str, Any]					  =       24
														_lowerCAmelCase	:				Optional[Any]					  =       16

							# labels
							if "rvlcdip" in checkpoint_url:
														_lowerCAmelCase	:				Tuple					  =       16
														_lowerCAmelCase	:				Union[str, Any]					  =       "huggingface/label-files"
														_lowerCAmelCase	:				Any					  =       "rvlcdip-id2label.json"
														_lowerCAmelCase	:				Optional[Any]					  =       json.load(open(hf_hub_download(a_							,					a_							,					repo_type="dataset"    )							,					"r"    )    )
														_lowerCAmelCase	:				Tuple					  =       {int(a_    ): v for k, v in idalabel.items()}
														_lowerCAmelCase	:				Any					  =       idalabel
														_lowerCAmelCase	:				str					  =       {v: k for k, v in idalabel.items()}

							# load state_dict of original model, remove and rename some keys
							_lowerCAmelCase	:				str					  =       torch.hub.load_state_dict_from_url(a_							,					map_location="cpu"    )["model"]

							_lowerCAmelCase	:				Any					  =       create_rename_keys(a_							,					has_lm_head=a_    )
							for src, dest in rename_keys:
														rename_key(a_							,					a_							,					a_    )
							read_in_q_k_v(a_							,					a_							,					has_lm_head=a_    )

							# load HuggingFace model
							_lowerCAmelCase	:				Union[str, Any]					  =       BeitForMaskedImageModeling(a_    ) if has_lm_head else BeitForImageClassification(a_    )
							model.eval()
							model.load_state_dict(a_    )

							# Check outputs on an image
							_lowerCAmelCase	:				List[str]					  =       BeitImageProcessor(
							    size=config.image_size							,					resample=PILImageResampling.BILINEAR							,					do_center_crop=a_    )
							_lowerCAmelCase	:				Tuple					  =       prepare_img()

							_lowerCAmelCase	:				Dict					  =       image_processor(images=a_							,					return_tensors="pt"    )
							_lowerCAmelCase	:				Optional[Any]					  =       encoding["pixel_values"]

							_lowerCAmelCase	:				Any					  =       model(a_    )
							_lowerCAmelCase	:				Dict					  =       outputs.logits

							# verify logits
							_lowerCAmelCase	:				int					  =       [1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8_192]
							assert logits.shape == torch.Size(a_    ), "Shape of logits not as expected"

							Path(a_    ).mkdir(exist_ok=a_    )
							print(F"Saving model to {pytorch_dump_folder_path}"    )
							model.save_pretrained(a_    )
							print(F"Saving image processor to {pytorch_dump_folder_path}"    )
							image_processor.save_pretrained(a_    )

							if push_to_hub:
														if has_lm_head:
																					_lowerCAmelCase	:				List[Any]					  =       "dit-base" if "base" in checkpoint_url else "dit-large"
														else:
																					_lowerCAmelCase	:				Dict					  =       "dit-base-finetuned-rvlcdip" if "dit-b" in checkpoint_url else "dit-large-finetuned-rvlcdip"
														image_processor.push_to_hub(
														    repo_path_or_name=Path(a_							,					a_    )							,					organization="nielsr"							,					commit_message="Add image processor"							,					use_temp_dir=a_							,					)
														model.push_to_hub(
														    repo_path_or_name=Path(a_							,					a_    )							,					organization="nielsr"							,					commit_message="Add model"							,					use_temp_dir=a_							,					)


if __name__ == "__main__":
						_snake_case						=    argparse.ArgumentParser()

						parser.add_argument(
						    "--checkpoint_url",
						    default="https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth",
						    type=str,
						    help="URL to the original PyTorch checkpoint (.pth file).",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
						)
						parser.add_argument(
						    "--push_to_hub",
						    action="store_true",
						)
						_snake_case						=    parser.parse_args()
						convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''






from __future__ import annotations

lowerCAmelCase									=   list[tuple[int, int]]

lowerCAmelCase									=   [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

lowerCAmelCase									=   ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right
class 		lowerCamelCase	:



	def __init__( self		,   a_		,   a_		,   a_		,   a_		,   a_		,   a_		,   ):
			lowerCAmelCase  :     int       						= pos_x
			lowerCAmelCase  :     Union[str, Any]       						= pos_y
			lowerCAmelCase  :     Optional[Any]       						= (pos_y, pos_x)
			lowerCAmelCase  :     Union[str, Any]       						= goal_x
			lowerCAmelCase  :     Tuple       						= goal_y
			lowerCAmelCase  :     str       						= g_cost
			lowerCAmelCase  :     Dict       						= parent
			lowerCAmelCase  :     List[Any]       						= self.calculate_heuristic()



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     int       						= abs(self.pos_x - self.goal_x						)
			lowerCAmelCase  :     Any       						= abs(self.pos_y - self.goal_y						)
			return dx + dy





	def __lt__( self		,   a_						):
			return self.f_cost < other.f_cost






class 		lowerCamelCase	:



	def __init__( self		,   a_		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= Node(start[1]		,   start[0]		,   goal[1]		,   goal[0]		,   0		,   a_						)
			lowerCAmelCase  :     Union[str, Any]       						= Node(goal[1]		,   goal[0]		,   goal[1]		,   goal[0]		,   99_999		,   a_						)

			lowerCAmelCase  :     Tuple       						= [self.start]
			lowerCAmelCase  :     list[Node]       						= []

			lowerCAmelCase  :     Tuple       						= False



	def 	_lowerCamelCase     ( self						):
			while self.open_nodes:
					# Open Nodes are sorted using __lt__
					self.open_nodes.sort()
					lowerCAmelCase  :     List[Any]       						= self.open_nodes.pop(0						)

					if current_node.pos == self.target.pos:
							lowerCAmelCase  :     Tuple       						= True
							return self.retrace_path(a_						)

					self.closed_nodes.append(a_						)
					lowerCAmelCase  :     Tuple       						= self.get_successors(a_						)

					for child_node in successors:
							if child_node in self.closed_nodes:
									continue

							if child_node not in self.open_nodes:
									self.open_nodes.append(a_						)
							else:
									# retrieve the best current path
									lowerCAmelCase  :     int       						= self.open_nodes.pop(self.open_nodes.index(a_						)						)

									if child_node.g_cost < better_node.g_cost:
											self.open_nodes.append(a_						)
									else:
											self.open_nodes.append(a_						)

			if not self.reached:
					return [self.start.pos]
			return None



	def 	_lowerCamelCase     ( self		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= []
			for action in delta:
					lowerCAmelCase  :     Dict       						= parent.pos_x + action[1]
					lowerCAmelCase  :     str       						= parent.pos_y + action[0]

					if not (0 <= pos_x <= len(grid[0]						) - 1 and 0 <= pos_y <= len(a_						) - 1):
							continue

					if grid[pos_y][pos_x] != 0:
							continue

					successors.append(
					    Node(
					        a_		,   a_		,   self.target.pos_y		,   self.target.pos_x		,   parent.g_cost + 1		,   a_		,   )						)
			return successors





	def 	_lowerCamelCase     ( self		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= node
			lowerCAmelCase  :     List[str]       						= []
			while current_node is not None:
					path.append((current_node.pos_y, current_node.pos_x)						)
					lowerCAmelCase  :     Dict       						= current_node.parent
			path.reverse()
			return path


if __name__ == "__main__":
					lowerCAmelCase									=   (0, 0)
					lowerCAmelCase									=   (len(grid) - 1, len(grid[0]) - 1)
					for elem in grid:
										print(elem)

					print("""------""")

					lowerCAmelCase									=   GreedyBestFirst(init, goal)
					lowerCAmelCase									=   greedy_bf.search()
					if path:
										for pos_x, pos_y in path:
															lowerCAmelCase									=   2

										for elem in grid:
															print(elem)



